System, method, and computer program product for reduction, optimization, security, and acceleration of computer data transmission using neural synchronization

ABSTRACT

By duplicating the human brain&#39;s neural synchronization process, the present invention increases overall process and communication efficiency of computer data records/frames by ten thousand percent (10,000%) or more over current techniques and methods for exchanging computer data.

COPYRIGHT NOTIFICATION

Portions of this patent application contain materials that are subjectto copyright protection. The copyright owner has no objection to thefacsimile reproduction by anyone of the patent document, or the patentdisclosure, as it appears in the Patent and Trademark Office, butotherwise reserves all copyright rights.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates, generally, to computer networks andcommunication systems and, more particularly, to the transport andprocessing of data records/frames in computer networks and communicationsystems.

2. Discussion of the Background

The industry's current methods and processing models used fortransmitting data across the network are antiquated and are inherentlyinefficient. Next-generation Cloud solutions are going to be expensiveto maintain, prone to failure, limited in scope, and require complexsecurity requirements to even function at a basic level. The World'sblind push for data standardization has focused purely on the humanaspects of information exchange in which data is inflated (metadata) toaccommodate the ability for humans to read it. Moreover, the data issent across the network one at a time in the exact same processingpattern as human verbal communication on a telephone. Words are spokenand sent one syllable at a time to be received and heard one syllable attime.

Nothing has been done to enhance or optimize the transportation aspectsof network data. As far as industry is concerned, the network is a dumbhighway that only routes data to needed destinations and has a plannedmaximum traffic load. It is a transportation system with nounderstanding of the data being moved and therefore has no ability toorganize or prioritize individual data loads. It is mindless processthat treats all data the same.

Sending vast amounts of data across the network using human verbalcommunication patterns is completely ludicrous and destined for failure.By modeling network communication on primitive human verbalcommunication, it has been crippled in its natural ability to optimizeprocessing. As a result, massive amounts of computer and networkresources are wasted in a rather expensive attempt to achievenext-generational cloud services.

Sending data across the network is a transportation business thatmeasures costs through total weight (bytes) and the number of packages(frames). Until industry sees it this way and allows their computersaccess to more advanced forms of data communication, they will continueto fail and the costs will continue to rise.

The currency used for determining network costs is measured in thenumber of bytes and number of frames transferred across the network overa given time period. A byte is how a computer represents an individualcharacter in digital format. Each of these bytes has a number of bits(1's and 0's), usually 8. When added together, they equal a digitalnumber that cross-lists to a table (code page/character set) thatproduces characters, some printable, some not. A network frame is thepackage/container that holds the actual data being transmitted acrossthe network. The frames are eventually converted to network packets.

Network bandwidth is actually sold and represented in Kbps (Kilobits persecond) or Mbps (Megabits per second). While the number of bits is goodfor measuring general network capacity, it is inappropriate for theunderstanding and measuring data costs. Individual bits is a binarytranslation concern, it has nothing to do with the formation ofcharacters and the number of frames needed to communicate differenttypes of data across the network. So, all data costs should be measuredin byte size per time-interval; such as, Kilobytes per minute orMegabytes per minute and network frames per minute.

bytes/time+(frames/time*network overhead)=costs.

The simple goal of network programming is to move the data from Point Ato Point B. For example, an oxygen sensor produces a measurement atPoint A and it is transmitted to a database server at Point B. Whathappens between A and B are where our costs are determined. The datacosts is incurred from the different computer processing levels thatgenerate all the bytes and frames necessary to prepare a data payloadfor network transmission. The payload consists of the original data plusany protocols or packaging needed to send it across the network. Whilethe goal of network programming is simple, its correct implementation ishotly debated. For the purposes of bandwidth reduction, the concern mustbe costs in bytes and costs in frames.

Network data is organized and sequenced with individual datarecords/frames. The record/frame is the basic storage unit and itcaptures a data event. A data event occurs when a data producer, such assensor, at some specific time produces a data record/frame. Each datarecord/frame consists of a number of data fields. The data fieldscontain all the information about the data event, such as its timestamp,name, measurement, type etc. A data field can be anything from images tosimple numbers. Both records and fields have varying degrees ofoverhead, which are extra bytes added to the frame payload to identifyand organize the data structure.

Each record also gets additional payload when it is translated into astructured messaging protocol. Messaging protocols are at the heart ofnetwork programming and contain all the commands necessary forcommunicating data reliably. The message, also known as the Data Frameis further translated into one or more network frames, and is ultimatelysent across the network. The network frame can be thought of as alocomotive pulling one box car full of data. A typical network frame is1518 bytes of which 18 bytes is for the locomotive and 1500 bytes is forthe box car which is called the MTU (Message Transfer Unit). If the DataFrame is too large, multiple locomotives will be required.

Calculating the cost of network communication can be broken down intothe following basic equations:

Messaging_Overhead+Record_Overhead+(Field_Total*(Average_Field_Size+Average_Field_Overhead))=Data_Frame_Size

Records_per_Minute*Data_Frame_Size=Payload_Bytes_per_minute

ROUNDUP(Data_Frame_Size/Network_Frame_Size)=Network_Frames

Records_per_Minute*(Network_Frames*(Frame_Overhead+Network_Overhead))=Network_Bytes_per_minute

(Payload_Bytes_per_minute+Network_Bytes_per_minute)*Cost_per_Byte=Total_Cost_per_minute.

The computer network industry is currently impaled on these equationsbecause of outdated and obsolete data processing methods. There is noreal conscious understanding of this data other than its basicstructure. Resource waste in everywhere occurring at every level.Industry, instead of trying to reduce some of these costs, has gone inthe opposite direction. Through unbridled data standardization andmeta-data accumulation, the Record_Overhead and Field_Overhead numbershave escalated with increases measured in hundreds, if not thousands, ofa percent. Internet Protocol network frame efficiency is not commonlypracticed by any levels of the application software industry. Thesefigures need to go in the opposite direction. Otherwise, large dataincreases, as seen with next-generation sensor networks, will eventuallygrind the entire Cloud to a stop.

The only form of data optimization currently available to the industrycomes in the form of basic pattern reduction, also referred to as datacompression. A compression algorithm removes consecutive repeated bytesand byte patterns within the data frame and provides a simple controlprotocol so they can be reinserted when the data payload is eventuallydecompressed. While these algorithms can be effective tools for reducingsome of the network bandwidth (10% to 40%), these gains are meaninglesswhen faced with the data growth curves projected by the next generationof sensor and automated smart systems (100% to 10,000%). Also, in mostsensor implementations where record size is small, compression doesabsolutely nothing to reduce the number of network frames with itsassociated network overhead.

Changing the cost equation requires a radical departure from the currentnetwork data processing model. In the current model each record isstateless, neither anticipated, nor predicted. There is no past, nofuture, just the present state of the data is known. This restriction isdevastating in its implication to higher levels of data efficiency andchains the cost equation around the necks of all future generations.When dealing with highly repetitive (time-series) data streams suchthose encountered in sensors networks, smart devices, and automationsystems, a human verbal communication pattern is neither practical, norfeasible.

Thus, notwithstanding the available hardware solutions, transportsoftware implementations, architectures, and middleware, there is a needfor a system, method, and computer program product that provides reducedbandwidth, increased speed, higher reliability, and better security inthe transmission and processing of data records/frames in computernetworks and communication systems. Further, there is a need for aprocessing system, method, and computer program product that providessuch reduced bandwidth, increased speed, higher reliability, and bettersecurity, (1) that can reduce, optimize, secure, and accelerate computerdata transport and processing, (2) that can more efficiently utilizeexisting bandwidth in communications systems and computer networks, (3)that is highly scalable, extensible, and flexible, (4) that canseamlessly integrate with any hardware platform, operating system, andany desktop and enterprise application, (5) that can seamlesslyintegrate with any data record/frame protocol, (6) that can beimplemented on any wired or wireless communication medium, (7) that canbe used to create human-level artificial intelligence and neuralinterfaces, (8) and that can eliminate over 99% of existing datarecord/frame communication and processing requirements.

SUMMARY OF THE INVENTION

The primary object of the present invention is to overcome thedeficiencies of the prior art described above by providing a system,method, and computer program product that can utilize a neuralsynchronization architecture to reduce, optimize, secure, and acceleratethe transmission and processing of data records/frames in communicationsystems, computer networks, and the applications utilizing those systemsand networks.

Another key object of the present invention is to provide a system,method, and computer program product that can more efficiently utilizeexisting bandwidth in communication systems and computer networks.

Still another key object of the present invention is to provide asystem, method, and computer program product that can reduce the amountof data bytes and network frames required to be transmitted incommunication systems and computer networks in order to processelectronic data records/frames through the use of thalamic motion.

Yet another key object of the present invention is to provide a system,method, and computer program product that can substantially increase theperformance and the end-to-end response time in communication systems,computer networks, and the applications that utilize those systems andnetworks to achieve real-time operation.

Still another key object of the present invention is to provide asystem, method, and computer program product that allows for theconversion of all computer data records/frames to thalamic motion,enabling a significant increase in system performance and reliabilityfor all data transmission and processing operations.

Still another key object of the present invention to provide a system,method, and computer program product that can reduce, optimize, secure,and accelerate the transmission and processing of data records/frames incommunication systems and computer networks that is designed toeliminate unnecessary network frame usage in the transport of computerrecords/frames and the overhead associated therewith.

It is yet another object of the present invention to provide a system,method, and computer program product for reduced, optimized andaccelerated data transmission and processing that is highly scalable,extensible, and flexible.

Yet another object of the present invention is to provide a system,method, and computer program product for reduced, optimized, secured,and accelerated data transmission and processing having an architectureand design that enables substantially seamless integration with anyhardware platform, operating system, and any desktop and enterpriseapplication.

It is a further object of the present invention to provide a system,method, and computer program product for reduced, optimized, secured,and accelerated data transmission and processing that can be implementedon any wired or wireless communication medium.

Another key object of the present invention is to provide a system,method, and computer program product that can more efficiently utilizeexisting bandwidth in communication systems and computer networks usinga neural synchronization algorithm for optimizing data recordtransmission and processing through the use of thalamic motion encodedin a protocol, referred to as motion signal protocol (MSP), that has thestructure to seamlessly integrate with any data record/frame protocol.

Yet another object of the present invention is to provide a system,method, and computer program product for reduced, optimized, secured,and accelerated data transmission that provides the operationalcharacteristics necessary for human level intelligence processingthrough artificial intelligence or a human neural interface.

Another key object of the present invention is to provide a system,method, and computer program product that duplications the architectureof the human brain in order to eliminate over 99% of the existing datarecord/frames associated with computer data network communication.

The present invention achieves these objects and others by providing asystem, method, and computer program product that implements the brain'sneural synchronization algorithm for reduction, optimization, security,and acceleration of data records/frames and processing in acommunication system or computer network, the system comprising one ormore computer devices running a motion decimation application module anda motion reactor application module, a motion replicator module forduplicating data, a motion aggregator module for integrating higherintelligence functions, and a management module for configuringresources and monitoring system operation. The motion decimationapplication and the motion reactor application are adapted tocommunicate through wired and wireless means in a computer network orcommunications system. A motion decimation application module is themeans through which data records/frames, such as data produced bycomputer network devices like sensors and data repositories likerelational databases, is translated from its original format intothalamic motion and further encoded with motion signal protocol (MSP)format for reduced, optimized, secured, and accelerated transport to amotion reactor application module. A motion decimation applicationmodule also receives synchronous reply data from a motion reactorapplication and translates the received data to motion synchronizationcommands and configuration requests. A motion reactor module performsthe functions of receiving thalamic motion data from a motion decimationapplication and sending reply data back to the motion decimatorapplication. A motion replicator module performs the function of usingthalamic motion to reproduce the original computer data record/frame.The motion aggregation performs the function of using thalamic motionwith additional notification signals for integration with higher formsof artificial and human intelligence.

The motion decimator application module in coordination with the motionreactor application module implement a neural synchronization processingframework capable to using thalamic motion to increase processing andcommunication efficiency for over ten thousand percent (10,000%).

Further features and advantages of the present invention, as well as thestructure and operation of various embodiments of the present invention,are described in detail below with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawing, which is incorporated herein and forms part ofthe specification, illustrate various embodiments of the presentinvention and, together with the description, further serve to explainthe principles of the invention and to enable a person skilled in thepertinent art to make and use the invention. In the drawing, likereference numbers indicate identical or functionally similar elements.

A more complete appreciation of the invention and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawing, wherein:

FIG. 1 is a functional block diagram of the architecture required forimplementing neural synchronization for the reduction, optimization,security, and acceleration of data records/frames and the use of thosedata records/frames in real-time analytical decision-making according tothe present invention.

FIG. 2 is a block diagram of the neural synchronization process of thehuman brain. The present invention is based on duplicating the brain'sdata processing framework. Neural synchronization is the algorithm usedto communicate enormous amounts of sensory and thought data to thedifferent lobes, cortexes, and layers throughout the brain in real-time.

FIG. 3 is a block diagram of dataflow between human eyes and the primaryvisual cortex. The diagram shows the neural synchronization process asit applied to the processing of human eyesight. In particular, thediagram shows the thalamic process for converting asynchronous sensorydata into a synchronous state which is a requirement for neuralsynchronization.

FIG. 4 is a screen capture showing the different configurationparameters necessary to implement a neural synchronization processaccording to the present invention.

FIG. 5 is block diagram of the Motion Signal Protocol (MSP) data frameused for transmitting thalamic motion. Neural synchronizationcommunicates using MSP in order to maintain a multicomponent synchronousstate.

FIG. 6 is a screen capture showing the operation of a simple artificialbrain using neural synchronization to process Internet of Things (IoT)sensor devices.

FIG. 7 is a functional block diagram of the dataflow of the motiondecimator application which is designed to duplicate the functionaloperation of the thalamus.

FIG. 8 is a functional block diagram of the dataflow of the motionreactor application which is designed to duplicate the functionaloperation of the primary visual cortex (V1).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following description, for purposes of explanation and notlimitation, specific details are set forth, such as particular networks,communication systems, computers, terminals, devices, components,techniques, data and network protocols, software products and systems,enterprise applications, operating systems, enterprise technologies,middleware, development interfaces, hardware, etc. in order to provide athorough understanding of the present invention. However, it will beapparent to one skilled in the art that the present invention may bepracticed in other embodiments that depart from these specific details.Detailed descriptions of well-known networks, communication systems,computers, terminals, devices, components, techniques, data and networkprotocols, software products and systems, enterprise applications,operating systems, enterprise technologies, middleware, developmentinterfaces, and hardware are omitted so as not to obscure thedescription of the present invention.

I. System Architecture and General Design Concepts

The design of the software for the system, method, and computer programproduct of the present invention takes a novel approach based uponduplicating the human brain's neurological synchronization algorithm.The system, method, and computer program product of the presentinvention reduces, optimizes, secures, and accelerates the transport andprocessing of data records/frames in communication systems and computernetworks through the use of thalamic motion. By encoding an object'sthalamic motion, the system, method, and computer program product of thepresent invention reduces standard data record/frame communicationresource requirements by over 99%. The system, method, and computerprogram product of the present invention uses Motion Signal Protocol,referred to as “MSP” to encode and process thalamic motion in a likemanner to human neural synchronization, thereby minimizing processingtimes, and increasing security, and increasing reliability, andenhancing the capabilities of artificial intelligence, and expanding thecapacity of existing computer networks and systems.

A. System Architecture

With reference to FIG. 1, a functional block diagram of the architecturefor a neural synchronization system 50 for reduction, optimization,security, and acceleration of data record/frame transport and processingis shown. The neural synchronization system 50 is comprised of aplurality of modules linked together to integrate into a communicationsystem or computer network. The system is highly modularized in order torealize more efficient operation and scalability in a distributedenvironment, to provide increased flexibility in implementation, tosupport significant growth in both functionality and capacity, and toreduce complexity. Due to the modular nature of the system 50, any newsoftware application can be developed and installed as an integratedcomponent without significant impact on existing functions. For example,new applications will not require full regression testing through theentire system. Testing can be limited to only the new components. As aresult, a significant reduction in life cycle cost can be achieved. Thearchitecture of system 50 provides a standards-based, modular, andexpandable system that incorporates new software technology to provideadditional capability and capacity. In particular, the system 50includes a motion decimator application module 51 and a motion reactorapplication module 52. The system 50 also includes a managementapplication module 58 for performing administrative functions of thesystem 50 including configuration, logging, auditing, and securityfunctions.

The decimator application module 51 is the means through which computersensory and/or network devices 57 input a data record/frame stream 58.The motion decimator application module 51 will break down each datarecord/frame stream 58 into a set of objects with associated data statesand will store this object information into memory 60. Memory 60 will beused to maintain the data state of each computer network device 57indexed by a unique identifier. The motion decimator application module51 will then compare future data record/frame stream 58 data against thestate maintained in memory 60 to produce thalamic motion 63. Thalamicmotion is a set of instructions that when applied will synchronize datastates. Thalamic motion will be used to synchronize the local data statein memory 60 with the remote data state in memory 60 of the motionreactor application module 52.

The motion reactor application module 52 performs synchronous executionof thalamic motion instructions. Upon receiving thalamic motion 63 fromthe motion decimator application 51, the motion reactor applicationmodule 52 will use the thalamic instructions to synchronize the datastate of memory 60. Upon conclusion of a synchronization cycle, themotion reactor application module 52 will trigger the motion replicatormodule 53 which will reproduce the original data record/frame stream 58and send it to a real-time or legacy data processing system 55. Themotion reactor application module 52 will then trigger the motionaggregator module 54 which will input the thalamic motion into anartificial or human intelligence system interface 56 for the purposes ofthought production. The structure and operation of the main applicationmodules 51 and 52 will be described in greater detail hereinafterfollowing a discussion of the general design concepts of neuralsynchronization.

B. General Design Concepts 1. Neural Synchronization

Life is by its own nature impaled on the “arrow of time”. All lifeformsmust be consciously aware of the passage of time. It is part of thebiological process and sits as a fundamental definition of what it meansto be alive. All lifeforms achieve a conscious state through perceptionof their environment using sensory systems. This perception isaccomplished through a rhythmic measurement of space. A biologicalprocess maintains state by performing and applying these measurementsbased on a linear time cycle. Since there exist no world clock(Einstein), the rhythm is set by each individual lifeform and measuredrelative to that lifeform (observer). So, existence is based on sensorymeasurements of 3-dimensional space relative to the time of observation.

In reference to FIG. 2, in the human brain, time relativity iscalibrated through the hypothalamus 12 which uses the optic charisma 11like a stellar pulsar to establish a circadian rhythm that regulates thecycling of the biological process. By performing this function, thehypothalamus 12 serializes/sequences sensory input and synchronizes theexecution of both the left and right brain hemisphere through timingconnections 24.

Each brain hemisphere is individually controlled by the thalamus 13. Thethalamus 13 cycles the linear firing sequence using a sophisticated setof nuclei internally sequenced. More importantly, the thalamus 13maintains a state of neural synchronization between itself and all thelobes, cortexes, and layers through these nuclei. The reason why allthalamic connections are reciprocal is because two-way communication ismandatory to maintain a synchronous state. Neural synchronization allowsthe brain to exist in a single entangled quantum state as sensorymeasurements are cycled, processed, and applied.

All sensory data 14 arrives asynchronously. The brain does not controlor synchronize the timing of the sensory observation points. Since thereare many different types of sensory systems that all generate data atdifferent intervals, the brain will not waste energy or capacitysynchronizing sensory data production. Instead, the thalamus 13 providesa bridge for all the asynchronous sensory data 14 into the brain'ssynchronous state.

Since the thalamus 13 has an understanding of the passage of time givento it by the hypothalamus 12, each of its individual nuclei can maintainstate. The thalamus 13 uses timed measurement intervals to translate allsensory data 14 into flat space-time (Minkowski). Basically, a singleslice of reality. Comparing the slice of space-time against asynchronized state allows the thalamus 13 to produce a measurement ofthalamic motion. Thalamic motion is actually a set of instructions onhow to change state and it doesn't matter what sensory or object formatis being measured. A downstream component simply applies theseinstructions to keep the states synchronized in real-time.

When the thalamus 13 conducts the measurement, it will detectchanges/movements in state and will categorize any detected motion aseither motionless, predictable, or unpredictable. Unpredictable statesare accumulated and their change instructions are encoded forsynchronization. Predictable and motionless states are discarded becausetheir results are already known by the downstream component due to itsshared state with thalamus 13. The knowledge is passed betweencomponents by the sheer existence of the timed neural pulse. Bydiscarding motionless and predictable motion states, the brain achievesincredible transmission efficiency and response time between itssynchronized components.

The thalamus 13 distributes the first level of derived thalamic motionin two directions. The first path is for sensory perception and thesecond path is for thought production. Since raw sensory perceptionrequires no further refinement, it can be synchronized directly to thestate maintained in the prefrontal cortex 20 through sensory perceptionbinding point 16. The prefrontal cortex 20 maintains a state within thebrain that can be described as the state of conscious reality. Humanconsciousness perceives and responds to the state that is maintained inthe prefrontal cortex 20. The prefrontal cortex 20 is the end point forboth sensory perception and thought production which are synchronized(bound) at different points within the hypothalamic/thalamic cycle.

The second direction for level 1 thalamic motion is thought production.Before a thought can be produced, it must go through ahierarchically-based system of intelligence production. The hierarchicalproduction process converts sensory data into cognitive objects foridentification, interpretation, and subsequent reaction. The thalamus 13is responsible for coordinating the firing sequence of all the higherlevels that will be executed by the different lobes of the brain,including the occipital lobe 21, parietal lobe 22, and the temporal lobe23. At all levels, the thalamus 13 will maintain state for that leveland share it with the connected component/lobe layer through synchronousconnections 18. By doing this, the thalamus 13 creates a hierarchicallystructured quantum state shared in by all of its biological components.Temporal lobe 23 will be fired towards the end of the thalamic cycle.Temporal lobe 23 has access to the memory subsystem where thoughtproduction is completed. Since the creation of a human thought has nodefined timeline and searches through the memory subsystem can takeadditional time, the thalamic cycle cannot wait for its production. Todo so would stall the brain and inhibit sensory perception. As a result,thought production is no longer synchronous and any thoughts producedmust arrive asynchronously at another location in the brain usingthought connection 19.

To reenter the neural synchronous state, asynchronous thought musttravel back to the hypothalamus in connection 12 so that it can besequenced and serialized. When communicating sequenced information,serialization guarantees that information arrives in that sequence. Thehypothalamus 12 will coordinate thought production from the temporallobe 23 in each hemisphere and synchronize it with the prefrontal cortex20 in thought binding point 17. The neural synchronization system 10represents the pinnacle of evolution in intelligence efficiency andspeed. By using neural synchronization, the brain can process enormousamounts of sensory and thought data in real-time using the least amountof energy and resources.

2. Neural Synchronization of Visual Sensory Data

Although neural synchronization applies to all forms and derived formsof sensory and thought data, in reference to FIG. 3, neuralsynchronization can be best examined by tracing the level 1 data flow ofthe human visual synchronization system 30. This is one of the firstlevels of the hierarchical quantum state and demonstrates the bridgingof asynchronous sensory data into the synchronous brain.

Visual Sensory Data as depicted by rabbit 31 is the origin point oflight. This light travels to and arrives at both the light and righthuman eye 32 at the same time. This is the observation point for visualsensory data and creates four (4) asynchronous data streams eachcontaining a 2-dimensional representations of a particular portion ofsight. These four data streams intersect at the optic charisma 33 wherethe streams crossover. Optic charisma 33 is the first point in the brainwhere visual sensory data is relayed. This mixes left and right humaneye 32 data together and sequences the subsequent processing of thesensory data by both the left and right brain hemispheres. Basically, itarranges the data in a linear sequence so that the two sides of thebrain can eventually sync up at the downstream binding points forconscious thought and sensory perception.

The optic charisma 33 also acts as the first filtering function invisual sensory data stream. The basics of visual sensory data is that itis composed of a mixture of cones and rods. The cones contain the visualdetails and the rods contain visual motion. As a cone moves in spacetheir appearance shifts to that of a rod. The Optic Charisma 33 usesthis information to produce four (4) asynchronous data streams. The twoprimary data streams 40 carry mixed left and right eye 32 datacontaining only rods, representing about 10% of the original data andare destined for the superior colliculus 34. The two secondary datastreams 41 carry mixed left and right eye 32 data containing cones androds that are destined for the lateral geniculate nucleus (LGN) 38. Theprimary streams 40 are the primitive foundation of sight and arecentered on the detection of visual motion. This part of the visualsubsystem was the first to evolve and provides an animal with an abilityto react to visual motion. The superior colliculus 34 uses informationfrom the cerebellum 35 which acts like a gyroscope to map motion (rods)to 3-dimensional space. The superior colliculus 34 uses this informationand other higher level reciprocals of this information to control neckand eye movement. The output of the superior colliculus 34 is a set ofrods aligned with the orientation of the head, body, and eyes. Theoutput is destined for the pulvinar nucleus 37 and the lateralgeniculate nucleus (LGN) 38 of the thalamus 36.

The secondary visual streams 41 are evolved sight processing and onlyavailable to higher life forms such as mammals, primates, and humans.These secondary streams carry the details of sight and are destined forthe LGN 38 of the thalamus 36. The LGN 38 applies the orientationknowledge coming from the superior colliculus 34 by mapping the rodsback into the secondary visual data stream. This aligns/stabilizes the2-dimensional data into 3-dimensional space before thalamic measurement.

Each nuclei of thalamus maintains state including the LGN 38. The dataitself creates the state. So, the LGN keeps a copy of the last sensorystate for the data produced by both the left and right human eyes 32.The thalamus 36 cycles the LGN 38 at a specific time interval. Thiscycle establishes the measurement time where all asynchronous input iscompared against the previous synchronous state. For visual input, thestate of all the cones and rods are measured. If there is a change instate, it will be classified as thalamic motion. Thalamic motion is aset of instructions on how to change state so that any connected braincomponent, in this case the primary visual cortex (V1) 39, can apply theinstructions to remain synchronized.

When you get to the occipital lobe, the cone and rod instructions becomeobject instructions escalated hierarchically (V1 thru V4). Here, eachlayer of the occipital lobe is measured for object motion. All thesedifferent forms of sensory motion are synchronized to a multi-modal,multi-layered state control maintained in the pulvinar nucleus 37 of thethalamus 36.

This is the central reason that intelligence has both bottom-up andtop-down characteristics. Before the thalamic motion is encoded fortransmission to the primary visual cortex (V1) 39, the motion isclassified as “Predictable” or “Unpredictable”. All predictable statesare discarded and only unpredictable states are encoded for neuraltransmission. In this Bayesian algorithmic framework rests the truepower of the brain. The thalamus 36 by removing motionless andpredictable motion data out of the data stream eliminates most of theincoming sensor data so that sensory perception can fit within thetiming window for conscious binding.

The problem when discussing the Bayesian nature of the brain is thatpeople's perceptions of prediction is based only on top-level cognitiveobjects. What is the chance a rabbit will jump left, right, or straight?While this is an activity of prediction, it misses the importance thatthat prediction plays in regulating and filtering sensory data withinthe thalamus 36. The rabbit is just a consolidated object that iscomposed of tens/hundreds of thousands of smaller predictions rangingfrom the chance that the rabbit will alter color to the chance thatthere will change in a visual cone. The brain's prediction is granularand has application on all levels.

As motion is bound up through components like the Occipital and ParietalLobe, it gains greater and greater hierarchical abstraction(objectification, states-within-states). For example, sensory patternsbecome fingers becomes hands becomes arms and so on. Top-down knowledgeis then passed back down the pulvinar nucleus 37 and used to groupsmaller objects for measurement; thereby, refining predictioncapability. So, the sensory motion of a finger is predictable if thehand is performing a certain activity that may have been predicted by aparticular arm movement.

By doing hierarchical predication in a synchronized state, the brainincreases data transfer efficiency by 100,000% or more as top-downinformation flows down to the visual sensory level 1 (geniculate/V1layer). This has significant implications for humans because theefficiency boost allows us to shift activity and subsequent energy useto higher brain functions. So, the less we have to do in the bottom ofthe brain results in the more we can do on top.

Once motionless and predicted motion data has been removed, theremaining unpredictable motion represents less than 1% of the originaldata size. This in the only information that is required in order tosynchronize the state. The final thalamic motion will then becommunicated in real-time using a neural connection 42 and used tosynchronize the state of the primary visual cortex 39.

Within the human visual sensory system 30 can be seen the primaryfunction of thalamus 36 is to bridge asynchronous sensory data to thesynchronous quantum state and to synchronize that state up through thevarious components of the brain. By performing this function, thethalamus 36 filters out all non-relevant visual sensory data and cantransfer information in real-time.

II. Structure and Architecture of System and Modules

A more detailed description of the structure and software architectureof the system and modules of the present invention is provided withreference to FIG. 1. Referring to FIG. 1, the software architecture ofneural synchronization system 50 is shown as implemented in a wired andwireless network, also referred to as a virtual network. The system 50and modules of the system can implemented in, be connected to, and/oruse any network or virtual network. Such networks and virtual networksinclude communication systems, such as local area networks, wide areanetworks, public access networks, internal computer bus networks, andother well-known systems, and the connections in such networks includephysical, logical, virtual links, or the like, wireless or wirelineconnections, all of which would be readily apparent to one of ordinaryskill in the art. The modularized design of the neural synchronizationsystem 50 facilitates the implementation of the system 50 in a varietyof network environments. Each of the modules described above and in moredetail hereinafter can be connected, individually, to the network forcommunication of data and information in operation of the neuralsynchronization system 50.

The brain is the most highly evolved information processor in existence.It can run circles around our fastest computers. The brain accomplishesthis feat not through overwhelming capacity, but through absoluteefficiency. The brain does not waste energy or resources in theprocessing of intelligence. So, every system, every subcomponent hasevolved to process information in the most efficient manner possible.

The brain is often referred to as the “Motion Muscle”. Thisgeneralization is essentially correct. The ability to detect, interpret,and react to motion is the primary function of the brain. For anylifeform to perceive motion implies a biological process that has theability to measure the passage of time. With humans, this isaccomplished by the circadian rhythm produced in the SCN of thehypothalamus. This rhythm regulates the brain's execution cycle which isused to serialize sensory and thought data to a linear time-line. Thisenables the measurement of the same data at two different points in timewhich is fundamental to perceive motion. So, all motion incorporate ameasurement of space based on time sequence.

Duplicating the brain's neural synchronization process requires twocomputer software applications running on one or more computer devices.These devices can be computer servers, desktop computers, laptopcomputer, network appliances, embedded devices, sensory platforms, orany hardware device that incorporates a central processing unit (CPU),memory storage, and input/output capability. Referring to FIG. 1, themotion decimator application module 51 runs on one computer device andis responsible for processing sensory and computer data input. Themotion reactor application module 52 runs on the same device or aseparate device and is responsible for processing sensory and computerdata output. The two computer software applications work in unisonduplicating the brain's neural synchronization process.

Since both software applications must be synchronized, the processbegins in neural management module 58. Module 58 provides an interfacethat sets the configuration for both the motion decimator applicationmodule 51 and the motion reactor application module 52. Configurationinformation can be interfaced and stored in the computer registry, aninitialization file (INI File), or in a relational database server. Thisprovides for any form of configuration input including computergraphical interface (GUI), a text editor, or a commercial databasemanagement interface.

In reference to FIG. 4, motion management module 58 shows a GUIinterface that sets many of the configuration controls that arenecessary to implement the neural synchronization algorithm. GUI section121 shows the general parameters necessary for synchronizing theconfiguration of module 51 and module 52. The Location parameter is aunique identifier to designate a pair of neural synchronizationcomponents. The Protocol parameter is currently set to Internet ofThings (IOT). Neural synchronization can function effectively againstany form of computer data protocol or data stream format. The twocomponents need to agree upon protocol format so the correct set ofparsing and reproduction tools/skills are applied. These computer dataprotocols can be preprogrammed or learned. The Input parameterdesignates the interface type for data input. The Input parameter iscurrently set to MySQL which is a standard commercial relationaldatabase interface. The Output parameter designates the interface typefor data output. The Output parameter is currently set to BENT which isa network socket implementation of a light-weight messaging protocol forInternet Protocol (IP) networks. The neural synchronization process canuse any form of transmission format and run on any form of computer orneural network. The next set of general parameters are associated withset diagnostic modes and various resets within the neuralsynchronization process.

GUI sections 122, 123, and 124 will be discussed in detail further on.GUI Section 125 contains the parameters required for the Input parameterselected. In the current selection, database access parameters arerequired. Each form of data input can have its own unique parametersthat are separate from the protocol variable selection. This allows theinterfacing of any form of data input regardless of protocol whether itcomes from a database, a data stream, a memory segment, optical disk,satellite transmission, or any form of internal or external datatransmission.

GUI Section 126 shows the communication parameters necessary forsynchronizing with up to two motion reactor applications. Neuralsynchronization only requires one motion reactor application to functioncorrectly. However, since the brain's neural synchronization is beingduplicated, the system is designed to incorporate two motion reactorsthat function similar to the left and right brain hemispheres. GUISection 126 contains parameters that are specific to communicationmessaging protocol used for sending thalamic motion. Any messagingprotocol can be used with a preference for light-weight messagingprotocols. GUI Section 126 will contain settings such as encryptionlevel or timeouts that are specific to the message protocol selected.Once the configuration parameters have been set and synchronized, themotion decimation application module 51 and motion reaction applicationmodule 52 can begin computer process execution.

Referring to FIG. 1, a computer network device 57 may be any one of anumber of different devices including a desktop computer, laptopcomputer, computer server, input/output device, personal digitalassistants (PDA), a pager, a mobile phone, IP phone, electronic watch,barcode scanner, digital camera, electronic sensor, smart home device,and other network enabled devices. A computer network device 57 willproduce a stream of asynchronous data record/frame stream 58. Stream 58will be composed of information encoded in some repetitive computer dataformat (protocol) produced and encoded according to some linear timestandard or interval. Referring to FIG. 3, the motion decimatorapplication module 51 reproduces the functionality of the pulvinarnucleus 37 and the lateral geniculate nucleus (LGN) 38. Thisfunctionality forms the central core of the neural synchronizationprocess. By combining these components, neural synchronization can beimplemented in a single software application. However, for incorporationinto higher brain simulations and hieratical implementations, thepulvinar nucleus 37 functionality would need to be moved to its ownseparate computer software application. It is principally responsiblefor prediction management and synchronization throughout the differentlevels of the brain and would need to be detached to in order tofunction efficiency. For most computer data applications, predictionmanagement and synchronization can be handled by the motion decimationapplication module 51 and the motion reactor application module 52.

The motion decimator application module 51 executes on a specific timingcycle. Referring to FIG. 4, GUI Section 122, the Cycle Time parameter inthis instance has been set to 3000 milliseconds or 3 seconds with aRetry Delay parameter set to 250 milliseconds or ¼ second for precisionadjustment. Since a quantum state is being maintained by the motiondecimator application module 51, the cycle must complete executionbefore another module 51 cycle begins; otherwise, overlapping cycleswill corrupt the synchronized quantum state.

Referring to FIG. 1, during each cycle the motion decimator applicationmodule 51 will first establish the measurement time which is a point fortemporal and spatial decorrelation. It is basically the spot where timeand space must freeze so linear measurements may occur. Time and spacewill remain frozen during the neural synchronization process. This ismandatory since the process involves synchronizing multiple componentstates separated by the speed of light. It is important to note that themotion decimator application module 51 and the motion reactor module 52exist in two different time frames. Like the human brain, the componentsare separated by distance (speed of light). Anytime space is measured,time must stop at the measurement point “Observer Effect”. This createsa problem trying to synchronize a second observation point such as themotion reactor application module 52. To overcome the problem of spatialand temporal dilation, module 51 and module 52 are entangled in order toshare state. By doing this, synchronous communication acts like a tunnelwithout time between the two components so they theoretically exist inthe same state in the same moment in time even though they are separatedby distance.

As one or more computer network devices 57 produce data records/frames58, the motion decimator application module 51 will store the incomingdata records/frames 58 in memory 60 organized and indexed by the uniqueID of computer network device 57. The individual components (data fieldstructures) will create memory objects each with their own data state.For example, an address data field would have an associated object andthe address data in the field would be the state. The composite of allthe data field states will represent the overall state of the computernetwork device 57.

As subsequent data record/frame 58 input arrives, the motion decimatorapplication module 51 will compare the state of the currently read datarecord/frame 58 with the previous record/frame state for computernetwork device 57 stored in memory 60. Any changes between the inputstate from record/frame 58 and the synchronized state from memory 60will be identified as thalamic motion 63. All other data will beclassified as motionless and discarded. If a state is motionless, thenthere is no need to communicate its data while in a synchronized state.Next, the motion decimator application module 51 will classify thethalamic motion 63 as either predictable or unpredictable. Predictablethalamic motion 63 will be discarded. If a state can be predicted, thenthere is no need to communicate its data while in a synchronized state.By sharing state, the motion decimator application module 51 and themotion reactor application module 52 generate and share predictionknowledge 64.

The motion decimator application module 51 takes the remainingunpredictable thalamic motion and encodes it into a Motion SignalProtocol (MSP) format to prepare it for transmission. MSP is a set ofinstructions that when applied will synchronize the two data statesmaintained in memory 60 of both module 51 and module 52. MSP will beexplained in greater detail further on. The motion decimator applicationmodule 51 will process and pack all unpredictable thalamic motion 63into a data frame for synchronous communication to the motion reactorapplication module 52.

Once a data frame is constructed and ready for transmission, the motiondecimator application module 51 will pass the data frame to thecommunication client module 62. The communication client module 62 willencode the data frame in a network messaging frame and will transmit thedata frame to the motion reactor application module 52. Since this is asynchronous communication process, the motion decimator applicationmodule 51 will enter a wait state until the data frame is processed bythe motion reactor application module 52 and acknowledged. Theacknowledgement signals that all MSP have been processed successfullyand that memory 60 in both module 51 and module 52 are synchronized.

The motion reactor application module 52 provides high-speed thalamicmotion network services to any number of motion decimator applicationsmodule 51. Each motion decimator application module 51 has a locationcode that uniquely identifies its operational and configurationrequirements to the motion reactor application module 52. The motiondecimator application module 51 unique ID is used by the authenticationprocess during server connection and forms the basis for providingdevice-to-device security and remote configuration. Authentication canbe composed of any number of levels and authentication sources that canauthorize device-to-device connection. The motion reactor applicationmodule 52 is available on-demand and will facilitate securecommunication sessions to the motion decimator application module 51,management agents, and other supporting software applications.

Once a motion decimator application module 51 has connected to a motionreactor application module 52, it will download any configuration 59 andprediction 64 information and will immediately begin transmittingthalamic motion 63 network data frames. The motion reactor applicationmodule 52 is a multi-threaded application server that launchesindividual motion reactor instances for each network data frame itreceives. Once activated, the motion reactor application module 52 willidentify the package's origin and protocol type and will set itselfaccordingly. The motion reactor application module 52 is designed torapidly execute motion signal protocol (“MSP”). As each network dataframe is unpacked, the motion reactor application module 52 applies thecontained MSP instructions to the object states stored and maintained inmemory 60. The MSP instructions provide all the necessary information tosynchronize the object states that are also being maintained in memory60 of the motion decimator application module 51. After all MSPinstructions for a given network data frame are applied, the motionreactor application module 52 will provide an acknowledgement back tothe motion decimation application module 51.

An MSP STOP instruction has special significance and will signal the endof the data stream of a single temporal experience. Upon receiving a MSPSTOP, the motion reactor application module 52 will perform motionprediction processing and will trigger the motion replication module 53and the motion aggregation module 54. To keep the states synchronized,all predictable motion must be applied to the state maintained in memory60 of the motion reactor application module 51. Once all predictions areapplied, the states will be synchronized.

Motion replicator module 53 is responsible for either regenerating theoriginal data protocol or generating an alternate form of data protocol.Module 53 provides the equivalent function of thalamus in that itregenerates sensory perception into prefrontal cortex memory. Forcomputer processing, this allows output of the neural synchronizationprocess to be either be synchronized with some type of real-timeinterface or integrated to a legacy data processing destination inmodule 55. Legacy in this definition is a data processing system that isincapable of interfacing synchronous thalamic motion. Since the originaldata stream arrived asynchronously, it has an option for departingasynchronously.

The motion replication module 52 can also be programmed to generate anytype of protocol, data structuring, or Standards-based format regardlessof its original format. While these data constructs have significantvalue to their final computer system destinations, they have littlevalue within the neural synchronization system 50. In the translation tothalamic motion 63, all of these metadata components would be classifiedas motionless and would be subsequently removed from the network dataframe. Whether these formats are added back in or substituted duringmotion replication is irrelevant to the process and doesn't affect thereduction, optimization, security, and acceleration of the datatransmission since they are not being transmitted anyway. So, the motionreplication module 52 can actively function as an inline protocol bridgebetween differing network data formats.

The motion aggregator module 54 is responsible for interfacing higherforms of intelligence both artificial and human based module 56. Theneural synchronization process incorporates thought production module 56that will provide prediction information regarding objects stored inmemory 60. While the motion decimation application module 51 is centeredon predicting individual object motion, the motion aggregation module 54is focused on consolidating multiple objects and measuring againstcomplex motion patterns. The brain's framework generates intelligencethrough a hierarchical framework of data states. The next level up forintelligence production is to begin to group the individual objects inmemory 60 in order to identify multiple object prediction patterns thatcan then be feed back down to the motion reactor application 52 withprediction 64 and synchronized with motion decimator application module51.

The motion aggregator module 54 will also classify and prioritize a setof motion notifications 65 destined for either conscious or subconsciousdecision-making. The following motions are used:

-   -   Low-value Predictable Motion—Signal for subconscious        decision-making    -   Low-value Unpredictable Motion—No signal sent, data ignored    -   Medium-value Predictable Motion—Signal sent for subconscious        decision-making.    -   Medium-value Unpredictable Motion—Signal sent for conscious        notification and possible priority escalation.    -   High-value Predictable Motion—Signal sent for both subconscious        and conscious decision-making.    -   High-value Unpredictable Motion—Signal sent for conscious        decision-making.

The notifications are intended to provide bridging with both artificialand human intelligence interfaces to value specific areas of the datastream to help prioritize decision-making. It is the brain's equivalentof focusing and defocusing attention.

Notifications are also used to throttle the size of aggregated thalamicmotion data. Since motion aggregation module 54 is concerned withmethods and techniques for combining data, this provides theintelligence necessary for selective consolidation based on informationvalue. The simplest form of data aggregation combines similar data froma single device. For example, a wireless biochemical sensor produces areading every second. Aggregation may accumulate three of these readingsbefore it produces one. The more complicated forms of data aggregationinvolve the combination of numerous dissimilar data sources. Forexample, there are 20 different sensors that are monitoring an entrygate. Aggregation may process all 20 data record/frames and produce asingle data record/frame of “ALL CLEAR”.

The motion aggregation module 54 can be used to throttle the amount ofdata relative to the amount of available system capacity and availablebandwidth. This is useful when events or special situations cause largedata spikes which have a tendency to overwhelm a network. Governing theamount of data inside of the dataflow prevents data overload bypreprocessing irrelevant data out of the stream by degrees. The motionaggregator module 54 can be programmed with a maximum output level andwill automatically adjust its OUTPUT level using various data analyticsto determine data relevancy and importance. The motion aggregator module54 can be programmed with a maximum INPUT level and will work in unisonwith the motion decimation application module 51 by triggering itsaggregation functionality. The motion aggregation module 54 protects thenetwork and computer infrastructure by guaranteeing a “never to exceed”specific data capacity limit.

To summarize, the motion decimator application module 51 will capture amoment in time for data measurement. The neural synchronization system50 can be best thought of as a system for processing linear experiences.The motion decimator application module 51 maintains object-state forany type of data that constitutes that experience. A computer networkdevice 57 can be any device on a network that produces datarecords/frames 58. Sensors and smart devices are the most prevalent. Atiming cycle is used to delineate the length of the experience. For eachexperience, the motion decimator application module 51 will:

-   -   quantify the experience to a specific time frame (temporal        decorrelation),    -   measure every object's (data record's) motion (spatial        decorrelation) arriving from any number of computer network        devices 57,    -   compare experience with previous experiences to produce thalamic        motion 63,    -   remove all motionless and predictable motion objects from the        data stream,    -   translate all remaining unpredictable motion to a set of motion        signal instructions,    -   pack thalamic motion organized by experience for network        transport,    -   transmit synchronous motion signals to the motion reactor        application module 52,    -   analyze, learn, and share prediction knowledge with the motion        reactor application module 52.

The motion reactor module 52 is a network server that functionson-demand and is initiated by the synchronous requests from the motiondecimator application module 51. When a motion request is received, themotion reactor application module 52 will:

-   -   unpack thalamic motion from the data frame,    -   restore the timing of the experience (temporal correlation),    -   apply all thalamic motion 63 to the existing object-state        (spatial correlation),    -   trigger predictable motion production,    -   trigger motion replication to either duplicate the original data        stream or bridge to another data stream,    -   trigger motion aggregation to interface higher intelligence and        generate prediction knowledge,    -   analyze, learn, and share prediction knowledge with the motion        decimator application module 51.

By using the brain's neural synchronization algorithm, the motiondecimator application module 51 and the motion reactor applicationmodule 52 exchange computer data in the exact same manner as the humanthalamus processes neural information to the various components in thebrain. This technique increases network data efficiency by three or moreorders of magnitude because 99% of data never actually has to betransported across the network to be comprehended and reproduced on theother side.

A. Motion Signal Protocol

Referring to FIG. 1, after a data record/frame 58 has been analyzed andits thalamic motion 63 has been identified for transmission, it willneed to be converted into Motion Signal Protocol (“MSP”). Each datarecord/frame 58 is wrapped with a unique identifier and a set of commandcodes that provides instructions for applying the thalamic motion data.The motion decimator application module 51 and the motion reactorapplication module 52 use request/reply communication modules 61 and 62where these motion transactions are sent and acknowledged in synchronousreal-time. The identifier provides reference to the data'sorigin/identification and the command code tells the motion reactorapplication module 52 what specifically to do with the data.

Using MSP, the motion decimator application module 51 provides themotion reactor application module 52 with specific instructions on howto reproduce the data state. Both processes use object state memory 60to maintain copies of previous data generational object-states. MSPkeeps these data sources completely synchronized and only requires thesmallest fraction of original record be sent across the network. If onlyone data field changes, then only one data field is transmitted. Thedata field change (thalamic motion 63) is all that is needed for themotion reactor application module 52 to synchronize data state.

The Following Motion Signal Codes are Currently Supported:

CYCLE_START—Signal marks the start of a data processing cycle and willcontain the Measurement/Decorrelation Time.

CYCLE_STOP—Signal marks the stop of a data processing cycle and willinitiate DEAD processing and trigger end of cycle processing.

SEED_OBJECT—Signal carries all the data fields of a record/frame and isused to seed the tracking process.

CHANGE_OBJECT—Signal carries only the changed data/frame fields whichare classified as unpredictable.

CAUSE-EFFECT_OBJECT—Signal carries derived cause and effect data and isused for exchanging prediction information.

DEAD_OBJECT—Signal identifies a data source that missed its scheduleddata production.

EXPERIENCE_RESET—Signal resets various states and other componentswithin the neural synchronization process.

To mimic the human process, the motion decimator application module 51cycles on a specific time interval to divide the data stream intospecific groups of data records/frames 58. Each data group represents asingle sensory experience. The motion decimator application module 51during each cycle will get the current time and produce a CYCLE_STARTmotion command. This establishes the start of the experience where timeis frozen (time decorrelation) and motion measurements are taken(spatial decorrelation). All data records/frames 58 produced up to thestart time will be compared with previous generations of data, convertedto thalamic motion, and packed into high-density datasets and encodedinto a data frame. Once all the data records within the experience areprocessed, the motion decimator application module 51 will create aDEAD_OBJECT for any data source that missed it scheduled production.Finally, a CYCLE_STOP command will be produced to end the experience.

Referring to FIG. 5, the MSP data frame 70 shows the general layout ofan MSP data frame. It carries an identifier 71 for Motion DecimatorApplication Module 51 and then a series of MSP instructions. Each MSPinstruction can be attached to a data segment that contains eitheradditional information or portions of the original data record/frame.MSP is designed to carry any originating input data record/frame wholeor in pieces.

Motion Signal Protocol provides the ability to convert 3-dimensionalspace measurements into a set of linear instructions that reflect thechanges in that space from one experience to the next. Theseinstructions then can be packed into a single data frame or split acrossmany data frames. Since the instructions are of linear nature, thecommunication of MSP data frames needs to be synchronous and serialized.

B. Motion Prediction

Referring to FIG. 1, prediction processing takes the following threeforms which can be mixed and can be applied in ascending or descendingorder and can be applied at all levels of hierarchical intelligenceproduction within a neural synchronization system 50.

1. Primitive Prediction

The primitive form uses a single generation of previous data to predictthe future pattern. There are many data fields in a computer record thatwill show primitive motion. For example, timestamps, sequence numbers,counters, and indexes all show motion. Their associated data fieldschange every iteration within the time-series data stream. However,their motion values can be predicted by using a single generation ofprevious data. For these data fields, the distance of change is measuredto identify a sequential increment/decrement that can be used to predictthe next change. A SEED Instruction will produce the initial value.After that, only the unpredicted sequential increment/decrement will beencoded. For example, with a time field, if the first value received was12:00:00 and the second value was 12:00:10, then the increment of ten(10) seconds would be sent. If the third reading is 12:00:20, then novalue needs shipping because the increment has been predicted. So, eventhough, the time field does exhibit actual thalamic motion, it doesn'trequire transmission because the neural synchronization system 50 usesprimitive prediction to project its future value.

2. Multigenerational Prediction

The multigenerational form uses two (2) or more generations of previousdata to predict the future data pattern. A single generation of data maynot reveal a motion pattern when the pattern stretches over multiplecycles of measurement. For example, when a radiation sensor detectsstandard background radiation, it may need several cycles to discernthis from an actual radiation threat. If we assume the sensor produces aone (1) through ten (10) measurement and we are going to compare fivegenerations of previous data to predict its future motion, consider thefollowing:

Sensor Cycle Series #1—1,1,0,1,1 (Clear)

Sensor Cycle Series #2—0,1,0,1,1 (Clear)

Sensor Cycle Series #3—2,0,1,1,1 (Alert)

Sensor Cycle Series #4—2,0,2,0,1 (Alert)

Sensor Cycle Series #5—1,1,1,1,1 (Clear)

Multigenerational sensor readings create specific motion patterns thatcan be learned. More importantly, the number of data generations thatare measured is equal to the number of data generations that can bepredicted. So, by measuring five (5) generations in the example above,we can now attempt to predict the next five (5) generations.

Multigenerational prediction creates a rather unique operationalcharacteristic of neural synchronization system 50 in that it allowssystem 50 to look forward in time in order to see back in time.Specifically, this form of prediction allows the neural synchronizationsystem 50 to output data faster than real-time input. In the exampleabove, system 50 can output the fifth (5th) measurement in response tothe first (1st) measurement in the predicted multigenerational sequence.So, by moving output forward in time, neural synchronization system 50can react to sensory data before its actual creation point.

This is exactly similar to the brain's operational framework forthalamic motion. In reference to FIG. 3, if the 1st measurement occursat the optic charisma 33, then the second (2nd) generation measurementwould be occurring at the observation point of the right and left humaneye 42. In other words, sensory perception would actually be occurringat observation time which would compensate for the time it takes thebrain to complete one cycle. Now, let's say that it takes 2 cycles forlight travelling from the creation point of rabbit 31 to reach the rightand left human eye 32. If the brain is outputting a fifth (5th)generation prediction, then sensory perception is running faster thanits real-time creation.

3. Hierarchical Prediction

The hierarchical form uses high-level consolidated objects to predictthe future pattern. To understand this hieratical concept consider thefollowing example: The first level of data states consist of 1000computer network device 57 s producing facility security sensor data.The next level up, which would correspond to the second layer of theoccipital lobe, would group the 1000 computer sensors into 100 rooms(objects). The third level would group the 100 rooms into 10 floors. Theforth level would group the 10 floors into 1 building. At each of theselevels, consolidated object intelligence can produce predictionknowledge that can be passed back down to level 1 to refine predictioncapability. A particular floor may reveal a particular predictionpattern; for instance, if the floor were empty late at night.Hierarchical Prediction has the effect of eliminating large portions ofthe level 1 sensory data because predicting high level objectsautomatically predicts all of their corresponding lower level objects inthe hierarchy. So, the floor prediction provides the room predictionswhich provides the corresponding computer network device 57 predictions.

C. Multi-Record Packaging

Current network optimization science is focused on reducing networkoverhead. Internet Protocol (IP) networks can generate a lot of overheaddepending on the paths taken by individual networks frames as they arerelayed around the network. While this pursuit has serious relevancy, itgains are insignificant against solving the true problem. The real issueis that the industry uses far too many network frames transmitting toolittle information.

When dealing with data records/frames 58, the current rational is thatonly one data record/frame 58 can be shipped across the network at atime. So, a data record/frame 58 will always require at least onenetwork frame. This is antiquated thinking and rooted in the humanrequirement of communicating one piece of information at a time. Forcingcomputers to send vast amounts of sensor and repetitive computer data ina two-dimensional manner reminiscent of a telephone call creates aproblem that cannot be fixed. Trying to reduce overhead inside of such amodel is the equivalent of bailing out a sinking ship with a teaspoon.

The best way to reduce network overhead is to break the one record-oneframe approach and package multiple data records/frames together into asingle network frame. This requires certain enhancements to the dataframe. Specifically, the data frame is redesigned to carry a dataset,instead of a data record. A dataset is basically a computer softwarememory model that allows records to be stored and accessed in multiplerows. Datasets are most commonly used for storing multiple records oncethey have been retrieved from a database server. Multi-record packagingallows a single network frame to carry large numbers of high densitymotion records eliminating unnecessary network frame waste.

The ratio of records to network frames will be dependent on notexceeding the maximum MTU size of 1500 bytes for typical IP networks.Exceeding this number will instantly double the amount of networkoverhead because an additional network frame will be required. So, thegoal is to get as many thalamic motion records in without going over toprevent uncontrolled frame fragmentation. With the Motion Decimator,which only extracts the changed data fields and records, a ratio from10-to-1 up to 1000-1 is possible. By increasing data density in the MTU,most data streams will be able to reduce network frame requirements by99%, jettison all the unnecessary network overhead, and acceleratenetwork data delivery to near real-time.

D. Dead Reckoning

To assume success in data processing requires a system that can countthe dead. When measuring generational data there is always a possibilitythat a given data generation goes missing or dies. In other words, thedata producer failed to create its scheduled data object on time or someother intermediate system failure occurred that may have destroyed therecord. Regardless, when expected data is missing out of the stream, itmust be classified as an abnormality and accounted for in any systemthat assumes success.

Referring to FIG. 1, both the motion decimator application module 51 andthe motion reactor application module 52 implement a system of DeadReckoning where missing data records/frames 58 are identified andprocessed. Dead Reckoning uses both the timestamp and the sequentialtime increment of a record to predict success. Module 51 begins with aCYCLE_START MSP instruction and attaches the current measurement time.This timestamp will become the basis for all Dead Reckoning calculationsperformed by both module 51 and module 52. After all its datarecords/frames 58 for a given cycle (time segment) are processed, themotion decimation application module 51 will perform the followingcalculation on all states that were not updated during the currentcycle:

IF (Current_Start_Time+Record_Time_Increment>=Last_Record_Time)

THEN create DEAD_OBJECT

Once the dead calculation is complete, module 51 will produce aCYCLE_STOP command.

When the motion reactor application module 52 receives a CYCLE_STARTcommand, it will simply store the decorrelation timestamp for later use.Module 52 will then process all the records in the cycle including thelist of DEAD_RECORDs. A CYCLE_START command has no relevancy unlessthere is a corresponding CYCLE_STOP command. The CYCLE_STOP command isthe final motion signal to conclude an experience and generate allpredicted data. At this point, module 52 will perform the followingcalculation:

IF (Cycle_Start_Time+Record_Time_Increment=Last_Record_Time) AND (!DEAD)

THEN generate predicted record

Network computer devices have success rates easily measuring above 99%.Dead Reckoning capitalizes on this success. By counting the dead, themotion decimator application module 51 provides the motion reactorapplication module 52 will all the knowledge it needs to reproduce thedata flawlessly with the least number of bytes required and in manycases no bytes at all. Module 52 performs all reactions in real-time andproduces synchronous acknowledgements to the Module 51 on the success orfailure of all MSP instructions. Failures in the process can result inself-diagnostics and resets in various components within the quantumstate of the neural synchronization system 50. These additionalprecautions are required because inaccurate dead reckoning can cause thereplication of fictitious data records/frames 58.

E. Calculating Time Relativity

Thalamic motion is based on a precise measurement of two (2) slices of3-dimensional space at two (2) points in linear time. In the human brainand in the motion decimator application module 51, these two points intime are created by a cycle time. All human and computer processes areidentical in the sense that they all must cycle to maintain state. A keyto understanding the calculation for thalamic motion is recognizing thedifference between the data creation cycle time and the measurementcycle time. These are two (2) different point in time which creates arelativity problem that must be accounted for when calculating thalamicmotion. The creation point is the time when data is first created in acomputer network device 57, such as when an oxygen sensor produces aperiodic reading. The measurement point is the time when the data ismeasured by the motion decimator application module 51.

Consider that a computer network device 57 may be producing data every10 seconds and there may be a thousand (1000) of these device 57's allproducing data at some interval of that 10 seconds. The device 57's arenot synchronized with the motion decimator application module 51 andtherefore exist in an asynchronous relationship. So, even if the motiondecimator application cycles at the same interval of 10 seconds, all thedifferent 57 devices may be at different points in the cycling of theirown data creation process, some faster and some slower. As a result, themeasurement cycle time of the motion decimator application module 51cannot be used for calculating thalamic motion. Instead, the motioncalculation must be performed using the observation cycle timing of eachindividual computer network device 57.

The motion decimator application module 51 compensates for relativity bycalculating each individual computer network device 57 based on the datarecord/frame creation time measured against its production cycle time.By doing the calculation this way, the motion decimator applicationmodule 51 allows the data from a computer network device 57 to enter asynchronous state while still preserving its asynchronous origin. Thiseffectively separates the measurement time of the motion decimatorapplication module 51 from thalamic motion calculations for computernetwork devices 57. This has special significance because it allows themotion decimator application module 51 to cycle at different speedsrelative to the computer network devices 57 without alternating thethalamic motion calculation.

Time relativity is also compensated within thalamic motion DEAD MSPinstructions. A DEAD instruction is created by the motion decimatorapplication module 51, so the measurement time must be preserved for useby the motion reactor application module 52. This preservation isperformed as part of the thalamic motion START MSP instruction whichcontains the measurement time. Dead reckoning calculations, aspreviously described, performed by the motion reactor application module52 will use the measurement time in order to correctly interpret thetime relativity to the creation point of the dead data record/frame.

F. Data Security

Thalamic motion is the safest method for secure data transportregardless of encryption level. It is a process where all repetitivedata and frames are removed from the network transmission. Multipleframes are then condensed into packed frames of non-repetitive data.These frames are finally encrypted and transferred in real-time acrossthe network. All methods and tools for breaking data encryption requirelarge volumes of highly repetitive sampling data in order to crack thecode. A thalamic motion stream just doesn't provide enough data of arepetitive nature for these algorithms to function correctly or at all.Thalamic motion encoding blocks an interception and decryption bylimiting time, limiting frames, limiting repetitive data, and limitingaccess.

More importantly, if the encryption were to be compromised, the use ofthe intercepted data would be severely limited. The two applicationsmodule 51 and module 52 maintain a state model of all the computernetwork devices 57 and that model determines how motion data isprocessed in and out of the data frame. Without this state model tointerpret the data record flow, much of the data captured would bemeaningless. It would be the equivalent of trying to determine thecontents of a high resolution image using just a handful of assortedpixels.

Thalamic motion is the principle reason that neural communication hasbeen so difficult to decipher in neuroscience. As an outside observerwho does not share synchronous state, there is no foundation tounderstand the significance of the motion encoding. For example, oneneuron may contain instructions for an entire image, a second may onlycarry instructions to update a few cones/rods, and a third carriesnothing. From an outsider's perspective, it appears chaotic.

A more significant benefit of thalamic motion is that it reduces thedata payload to such a degree that there is excess bandwidth availableto implement the following two new forms of data security:

1. Ghost Data Security

Ghost Data is a process for adding fictitious data frames or datarecords to the data stream in controlled amounts. The Decimator createsa ghost data frame or record and inserts it into the data stream. TheReplicator identifies these ghost frames and records and automaticallyremoves them. Ghost data is based on a copy of the original data frameor record but will have its final contents systematically altered. Useof a Ghost Data stream makes it virtually impossible to distinguish whatdata is real since the framing cycle and packing sequence will only beknown to the motion decimator application module 51 and the motionreactor application module 52 which negotiate and maintain their owninternal rotating combination. Referring to FIG. 4, GUI section 126, theGhost Parameter sets the amount of ghost data frames that aretransmitted with the thalamic motion data.

2. Picture-in-Picture Security

Sensor-based data streams when measured together at any single point intime create a situational picture. Accessing and coopting that sensorypicture is a vulnerability often portrayed in movies where interceptingthe sensor network and changing its readings provides access to topsecret facilities. Guards are completely unaware because their monitorsshow nothing. This type of internal attack is a genuine risk and ithighlights the fact that sensor data streams are highly vulnerable toattack and many of these attacks can go undetected.

Picture-in-Picture security is designed to prevent internal attacks byfooling the attackers. The motion decimator application module 51 willcreate a fabricated data stream where a specific pre-defined situationalpicture is exposed. The real decimated data stream is actually hiddeninside of the false data stream. Attackers will see a completelyfictitious sensor picture. Any manipulation of that picture will have noeffect on standard operations and will instantly be detected.

III. Operation of System and Modules A. Self-Diagnostic Analysis

Time is a major impediment in analyzing the results of a computerimplementation of the neural synchronization process. Referring to FIG.1, this is especially the case when analyzing a shared quantum state inreal-time which is the primary function of neural synchronization system50. Any attempts to get scientific measurements slows the timing of thecycle of the motion decimator application module 51 and subsequentoperation of the motion reactor application module 52. Since thealgorithmic model is based on linear time measurements, these outsidemeasurements corrupt the data sampling. Furthermore, any attempts toperform post-analysis on the shared quantum states is useless. Since theanalysis is occurring outside of real-time, it can only producesuperficial results that may not be reflective of the true quantumstates at the various points in the time.

The problem rest in the fact that to properly diagnose and analyze theoperation of neural synchronization requires that a connected componentparticipate in the synchronous relationship in order to performreal-time data analytics, the same way the brain does. Thisparticipation makes it a part of the neural synchronization cycle andallows the component to perform real-time data analytics in the correctsequence at the right moment in time.

The solution rests in understanding that analytics is part of adecision-making process that requires some form of interpretation.Neural synchronization system 50 duplicates the function of the humanthalamus and is therefore incapable of performing interpretation. Whilesystem 50 uses interpretation, such as object identification and motionprediction, to enhance its communication efficiency, it cannot makethese interpretations. Interpretation is a function of the consciousmind which can only be performed in the frontal lobe of the brain. So,the requirements for self-diagnostic analysis can only be solved bysimulating a frontal lobe process and attaching it to the neuralsynchronization cycle. In other words, we have to add another braincomponent that is fast enough and in sequence to analyze the quantumstate of system 50 in real-time.

The brain can be simulated using a graphical user interface softwareprogram that can cycle the motion decimation application module 51 insequence with a data simulator and data analyzer. The analyzer is partof a frontal lobe decision-making process and is functioning as asensory perception module 55. Basically, module 55 shares in the quantumstate of the neural synchronization process. Module 55 then can performreal-time analytics to validate the individual components that make upthat quantum state. In reference to FIG. 4, GUI section 123 shows theconfiguration for data analyzer as either a synchronous or asynchronouscomponent. GUI section 124 shows the configuration information for thenetwork device simulator. To provide accurate diagnostics, the analyticsrequire the ability to set data patterns and measure it againstpredicted results to ensure that the process is correctly synchronizingthe quantum state between components.

Referring to FIG. 1, the graphical user interface GUI that duplicatesthe brain will need to cycle the following three (3) components:

-   -   Computer network device 57 simulator    -   Motion decimation application module 51    -   Sensory perception module 55 analyzer

Referring to FIG. 6, GUI Sections 131, 132, and 133 show output fromthese three (3) components, respectively. GUI Section 133 shows theresults of the self-diagnostic. Here, module 55 is displaying the sharedquantum state. From this state, module 55 compares the replicated datarecord/frame stream 58 with the original data record/frame stream 58 totest the accuracy of its reproduction.

B. Thalamic Motion

While standards have been established and generally accepted by theindustry for network access—i.e., the physical, data link, and networklayers—and most all systems and applications provide for communicationusing Transmission Control Protocol/Internet Protocol (TCP/IP)—i.e., IPrunning at the OSI network layer and TCP running at the OSI transportlayer, there is severe fragmentation and lack of industry adoption andagreement with respect to a protocol or language for interfacing withTCP/IP and the layers above the transport layer in the OSI model—i.e.,the session, presentation, and application layers.

As a consequence of this lack of a universal protocol or language,numerous and varying protocols and languages have been, and continue tobe, adopted and used resulting in significant additional overhead,complexity, and a lack of standardization and compatibility acrossplatforms, networks, and systems. This diversity in protocols andlanguages, and lack of a universal language beyond the transport layer,forces the actual data being transported to be saddled with significantadditional data to allow for translation as transmission of the dataoccurs through these various layers in the communication stack. The useof these numerous and varying protocols and languages create and,indeed, require additional layers and additional data for translationand control, adding additional overhead on top of the actual data beingtransported and complicating system design, deployment, operation,maintenance, and modification. The use of these numerous and varyingprotocols and languages also leads to the inefficient utilization ofavailable bandwidth and available processing capacity, and result inunsatisfactory response times.

Referring to FIG. 1, the inventor of the neural synchronization system50 of the present invention recognized the severe fragmentation and lackof industry adoption and agreement with respect to a protocol orlanguage for interfacing with TCP/IP and the layers above the transportlayer and the deficiencies caused thereby, and developed a protocol foruniversal data payload delivery. The architecture and design of neuralsynchronization system 50 of the present invention rests on the primarypremise of a commonly understood principle of agnostic data description,requiring a protocol for universal data payload delivery. Thus, theinventor of the neurological system 50 of the present inventiondeveloped a simple protocol to incorporate all the other protocols,referred to as the motion signal protocol (MSP) for encoding thalamicmotion 63 as previously described.

To understand the operation of thalamic motion 63 within a neuralsynchronization process requires that system 50 be applied to a givenprotocol or format from which the differences in data communicationtechniques can be quantified. To achieve this, neural synchronizationsystem 50 was applied to an Internet of Things (IoT) data protocol.Referring to FIG. 6, the last line of GUI section 132 shows a 99.52%reduction in network frames using neural synchronization as opposed tocurrent computer communication methods. What follows is a breakdown ofhow neural synchronization was able to perform this significantbandwidth reduction using IoT data records/frames.

C. Thalamic Motion Byte and Frame Reduction Formula

To project bandwidth and frame reductions using thalamic motion 63requires an understanding of the amount of data currently being producedfrom an IoT system and some of the unique record characteristicsassociated with that data.

Calculations are based on the following information (all data sizescalculated in bytes):

-   MO=Message Overhead    -   MO is composed of the network message protocol. This is the size        in bytes of the light weight messaging or custom protocol needed        to send the data across the network.-   RO=Record Overhead    -   RO is composed of all the record control header and record        structural data.-   TDF=Total Data Fields    -   TDF is the number of data fields in the record.-   AFS=Average Field Size    -   AFS is the average size of the data fields.-   AFO=Average Field Overhead    -   AFO is the average size of field overhead. Each field will        contain extra bytes in one form or another to identify the        individual field.-   VDF=Value Data Fields    -   VDF is the number of value fields. Within a record, most of the        data fields are used for identification and processing. The real        value of the record rest is one or two data fields. Decimation        concerns itself with these fields. The rest of record is        composed of either repetitive data fields or fields with        predicable data patterns.-   AVS=Average Value Size    -   AVS is average size in bytes of the value fields-   AVO=Average Value Overhead    -   AVO is average size of field overhead needed to identify the        value data field.-   MPS=Motion % per Sample    -   MPS is the one of the keys to subconscious data processing. It        is the percentage that a value field will change. The motion        decimator application module 51 measures change by its own        sampling cycle since part of its function is to divide the data        stream into time segments (temporal decorrelation). This number        needs to correspond with the time measurement for the formula        below. For example, if the motion decimator application module        51 is cycling every 3 seconds (20 times a minute) and the desire        is to measure bytes per minute, then the Motion % Per Sample        needs to be divided by the cycles per minute. So, if a record        changes 20% per minute then its chance per change per cycle at 3        seconds is 0.2/20=0.01 or 1%. If a record changes 100% per 3        second cycle, then its overall change will be 1*20=20.00 or        2000% per minute.-   DFS=Data_Frame_Size    -   DFS is size in bytes of the fully network packaged data record.        The software application has finished its job and the next step        is to hand the data frame to the network for processing.-   NFS=Network_Frame_Size (1500 bytes)    -   NFS is the standard network frame size. In most cases, it is        1500 bytes.-   PFR=Packed Frame Ratio    -   PFR is the number of data records that fit into one data frame.        Decimation performs multi-record frame packaging based upon        available network space and topology.-   MCF=Message Control Frames    -   MCF is the total number of extra frames needed to confirm        transmission. Most messaging control protocols will send data        with one frame and will receive an acknowledgment with another        frame. The message control frames do not generally contain many        bytes, but they are mandatory to ensure the synchronization and        confirmation of data transfer and execution.-   RNF=Required Network Frames    -   RNF is the total number of network frames needed to move a data        frame.-   RPM=Records per Minute    -   RPM is the total number of records per minute produced by a data        source.-   x=Number of Data Sources

Standard Data Processing

-   -   Data Frame Size (DFS)=MO+RO+(TDF*(AFS+AFO))    -   Required Network Frames (RNF)=ROUNDUP (DFS/NFS)    -   Standard Byte Requirements (y₁)=DFSx*RPM    -   Standard Frame Requirements (f₁)=((RNF+MCF)x*RPM)

Thalamic Motion Processing

-   -   Data Frame Size (DFS)=(MO/PFR)+(RO+(VDF*(AVS+AVO)))*MPS    -   Required Network Frames (RNF)=ROUNDUP (DFS/NFS)    -   Decimated Byte Requirements (y₂)=DFSx*RPM    -   Decimated Frame Requirements (f₂)=((RNF+MCF)/PFR)x*RPM)

Network Reduction

-   -   Byte Reduction Percentage=1−(y₂/y₁)    -   Frame Reduction Percentage=1−(f₂/f₁)

The following estimates were conducted using IoT data frames processedby a multi-protocol router into a local relational database and thenrelayed across the network to another relational database. Calculatingthe data frames between the databases was done using a standard SQLinterface, a MQTT interface, and a neural synchronization interface. Inorder to standardize the results, the sampling consist of 1000 CarbonMonoxide sensors producing IoT data frames at various intervals over thecourse of 1 minute. The following results were calculated on a perminute basis:

Motion % Sampling Packed SQL MQTT Decimation Sensor per Rate Frame(Data) (Data) (Data) SQL MQTT # Sample (Seconds) Ratio (Frames) (Frames)(Frames) Reduction Reduction 1000  2% 3 1000 4,902 KB 5,469 KB 10.5 KB99.786% 99.808% 40,000 FR 80,000 FR 40 FR 99.900% 99.950% 1000  2% 51000 2,941 KB 3,281 KB 6.3 KB 99.786% 99.808% 24,000 FR 48,000 FR 24 FR99.900% 99.950% 1000  2% 10 1000 1,471 KB 1,641 KB 3.15 KB 99.786%99.808% 12,000 FR 24,000 FR 12 FR 99.900% 99.950% 1000  2% 30 1000 490KB 547 KB 1.05 KB 99.786% 99.808% 4,000 FR 8,000 FR 4 FR 99.900% 99.950%1000  2% 60 1000 245 KB 273 KB 0.52 KB 99.786% 99.808% 2,000 FR 4,000 FR2 FR 99.900% 99.950% 1000  5% 3 1000 4,902 KB 5,469 KB 25.1 KB 99.487%99.540% 40,000 FR 80,000 FR 40 FR 99.900% 99.950% 1000  5% 5 1000 2,941KB 3,281 KB 15.1 KB 99.487% 99.540% 24,000 FR 48,000 FR 24 FR 99.900%99.950% 1000  5% 10 1000 1,471 KB 1,641 KB 7.54 KB 99.487% 99.540%12,000 FR 24,000 FR 12 FR 99.900% 99.950% 1000  5% 30 1000 490 KB 547 KB2.51 KB 99.487% 99.540% 4,000 FR 8,000 FR 4 FR 99.900% 99.950% 1000  5%60 1000 245 KB 273 KB 1.26 KB 99.487% 99.540% 2,000 FR 4,000 FR 2 FR99.900% 99.950% 1000 10% 3 500 4,902 KB 5,469 KB 50.3 KB 98.975% 99.081%40,000 FR 80,000 FR 80 FR 99.800% 99.900% 1000 10% 5 500 2,941 KB 3,281KB 30.2 KB 98.975% 99.081% 24,000 FR 48,000 FR 48 FR 99.800% 99.900%1000 10% 10 500 1,471 KB 1,641 KB 15.1 KB 98.975% 99.081% 12,000 FR24,000 FR 24 FR 99.800% 99.900% 1000 10% 30 500 490 KB 547 KB 5.03 KB98.975% 99.081% 4,000 FR 8,000 FR 8 FR 99.800% 99.900% 1000 10% 60 500245 KB 273 KB 2.51 KB 98.975% 99.081% 2,000 FR 4,000 FR 4 FR 99.800%99.900% 1000 25% 3 200 4,902 KB 5,469 KB 126 KB 97.436% 97.702% 40,000FR 80,000 FR 200 FR 99.500% 99.750% 1000 25% 5 200 2,941 KB 3,281 KB75.4 KB 97.436% 97.702% 24,000 FR 48,000 FR 120 FR 99.500% 99.750% 100025% 10 200 1,471 KB 1,641 KB 37.7 KB 97.436% 97.702% 12,000 FR 24,000 FR60 FR 99.500% 99.750% 1000 25% 30 200 490 KB 547 KB 12.6 KB 97.436%97.702% 4,000 FR 8,000 FR 20 FR 99.500% 99.750% 1000 25% 60 200 245 KB273 KB 6.28 KB 97.436% 97.702% 2,000 FR 4,000 FR 10 FR 99.500% 99.750%1000 50% 3 100 4,902 KB 5,469 KB 251 KB 94.873% 95.404% 40,000 FR 80,000FR 400 FR 99.000% 99.500% 1000 50% 5 100 2,941 KB 3,281 KB 151 KB94.873% 95.404% 24,000 FR 48,000 FR 240 FR 99.000% 99.500% 1000 50% 10100 1,471 KB 1,641 KB 75.4 KB 94.873% 95.404% 12,000 FR 24,000 FR 120 FR99.000% 99.500% 1000 50% 30 100 490 KB 547 KB 25.1 KB 94.873% 95.404%4,000 FR 8,000 FR 40 FR 99.000% 99.500% 1000 50% 60 100 245 KB 273 KB12.6 KB 94.873% 95.404% 2,000 FR 4,000 FR 20 FR 99.000% 99.500% 1000100%  3 50 4,902 KB 5,469 KB 503 KB 89.745% 90.807% 40,000 FR 80,000 FR800 FR 98.000% 99.000% 1000 100%  5 50 2,941 KB 3,281 KB 302 KB 89.745%90.807% 24,000 FR 48,000 FR 480 FR 98.000% 99.000% 1000 100%  10 501,471 KB 1,641 KB 151 KB 89.745% 90.807% 12,000 FR 24,000 FR 240 FR98.000% 99.000% 1000 100%  30 50 490 KB 547 KB 50.3 KB 89.745% 90.807%4,000 FR 8,000 FR 80 FR 98.000% 99.000% 1000 100%  60 50 245 KB 273 KB25.1 KB 89.745% 90.807% 2,000 FR 4,000 FR 40 FR 98.000% 99.000%

D. System Operation—Functional Sequence

Referring back to FIG. 1, the end-to-end flow duplicating neuralsynchronization and the processing of thalamic motion 63 of the presentinvention is described. With reference to the diagrams of FIG. 7 andFIG. 8, the steps in the neural synchronization process system 50 are asfollows:

1. In reference to FIG. 7, the motion decimator application module 51executes on a specific timing interval and will execute one dataprocessing cycle per startup request.

2. The cycle starts with module 81 and will attempt to set themeasurement time.

Safety checks are performed to ensure that all previous cycle processingwas successfully completed. If not, the previous data cycle will becompleted and resynchronized before the next sequence is started. Safetychecks are performed on the network connection and session state of themotion reactor application module 52 referring to FIG. 1. If everythingis correct, the motion decimator application module 51 will get thecurrent system time to establish a CYCLE_START time, also known as thetemporal decorrelation time or measurement time. Referring to FIG. 7, adata frame 94 will be constructed that will contain a dataset and thefirst record of the dataset will be a CYCLE_START Instruction.

3. Data records/frames are read one at a time by module 83. All datarecords/frames created up until the set measurement time will beprocessed. Records/frames created after the set measurement time will beprocessed in the next cycle.

4. For each record/frame read, module 84 will search the object statememory 85 for a previous record/frame state.

5. If no previous state exist for a current record/frame, then module 87will create a SEED Instruction and will create a new entry in the objectstate memory 85. The SEED Instruction provides the necessary data torecreate the new entry in any synchronized downstream object statememory 85. A SEED will include the entire original data record/frame.The SEED will be written to the data frame 94.

6. If a previous state exist in the object state memory 85 for a currentrecord/frame, then module 86 will measure the motion between the currentdata state and the previous data state by comparing each individual datafield. Motion is derived by measuring data states of time-series data ata fixed time interval. Any changes in the data byte patterns will beidentified as thalamic motion. All other data will be classified asmotionless and discarded.

7. If motion is detected, then the motion is analyzed for prediction. Ifthe motion of the data record/frame is predictable, the motion will bediscarded. If the motion is unpredictable, module 88 will create aCHANGE Instruction and will update the corresponding entry in the objectstate memory 85. The CHANGE will be written to the data frame 94.

8. Once the supply of data records/frames has been exhausted for a giventime slice, the decimation system application module 51 will executemodule 89 to determine dead data records/frames. Module 88 searches theobject state table to find states that were not updated during thecycle. These are candidates for dead states. Module 89 then measurestheir motion to determine if they really dead or just not currentlyscheduled for production. If they are dead, then module 92 updates theobject state memory 85 and a DEAD Instruction is written to the dataframe 94.

9. Once dead processing is complete, the decimation application system51 will execute module 93 to create a STOP Instruction. This concludesthe linear encoding sequence for all data input for a given cycle.Module 93 will update object state memory 85 and a STOP Instruction iswritten to the data frame 84

10. The final step of the cycle is to initiate module 95 for synchronouscommunication. If should be noted that module 95 can also be initiatedprior to reaching a STOP Instruction. All these Instructions are MotionSignal Protocol (MSP) and will be written into the dataset of the dataframe up to either the maximum packing size or the maximum network framesize. If either of these conditions occur, the motion decimatorapplication module 51 will send the data frame 94 to the motionreplicator application module 52, referring to FIG. 8 and create a newdata frame to hold the additional MSP.

11. In reference to FIG. 8, the motion reactor application module 52contains a multi-threaded communication server module 101. Communicationserver module 101 maintains network connections and will listen fornetwork requests from connected clients including the motion decimatorapplication module 51. Upon receiving a network request, module 101 willlaunch an appropriate module to process and interpret the received dataframe 112.

12. For network requests originating from motion decimator 51 module,motion reactor 52 will launch a Read Decimation Request module 102 toprocess the data frame 112. The data frame 112 is interpreted asdepicted in FIG. 5 where it is organized into a dataset containing alinear sequence of Motion Signal Protocol (MSP) ordered first-infirst-out (FIFO). Module 102 will unload and process each MSPinstruction.

13. If a MSP CYCLE_START is encountered, Module 103 with set therelative measurement time for all MSP instructions until the next MSPCYCLE_STOP. In order for the motion reactor 52 to stay synchronous, allmotion interpretation and calculation must be performed at the same timerelativity as the measurement point of motion decimator applicationmodule 51. Basically, the motion reactor application module 52 runs onthe time set by the motion decimator application module 51. This timesetting is transmitted in the MSP CYCLE_START instruction.

14. If a MSP SEED_OBJECT is encountered. Module 104 will add a new entryto object state memory 113. The SEED_OBJECT carries the entire contents(all data fields) of original data record/frame.

15. If a MSP CHANGE_OBJECT is encountered. Module 105 will update anexisting entry in object state memory 113. The CHANGE_OBJECT carries anyindividual data fields that contain unpredictable motion. Module 105will apply these changes in addition to any automatically generatedpredictable motion data fields for that object.

16. If a MSP DEAD_OBJECT is encountered, Module 106 will mark anexisting object in object state memory 113 as dead (non-responsive)which is a state indicating that an object missed its scheduled dataproduction.

17. If a MSP RESET_OBJECT is encountered, Module 107 will reset one ormore objects in object state memory 113. The RESET_OBJECT carries scopeinformation as to the extent of the reset requested. It may apply to asingle field, a single entry, or the entire memory.

18. If a MSP_STOP_CYCLE is encountered, Module 108 will initiate end ofcycle processing which includes updating predictable records/framesModule 109, replicating records/frames Module 110, and aggregatingintelligence Module 111.

19. Module 109 will update records/frames with predictable motion. Mostdata records/frames have no unpredictable motion and therefore will notgenerate CHANGE_OBJECTS. As a result, these data records/frames must beidentified so that their predictable motion can be applied. Once a cyclehas concluded, a search of the object state memory 113 will provide alist of all non-dead entries that need predictable motion generation.

20. Module 110 will perform motion replication services where thethalamic motion can be converted back into the original dataframe/record format.

21. Module 111 will perform data aggregation services where the thalamicmotion can be integrated into artificial and human intelligenceinterfaces.

22. When data frame 112 has been fully processed, module 114 willprovide a communication acknowledgement to the motion decimatorapplication module 51.

23. Referring to FIG. 1, on receiving a motion reactor module 52acknowledgement, the motion decimator module 51 will unblock and willeither start construction of a new data frame or if a CYCLE_STOP wassent and acknowledged, then the motion decimator application module 51will conclude its neural synchronization cycle.

The system, method, and computer program product of the presentinvention can be implemented on any wired or wireless communicationmedium including, but not limited to, satellite, cellular, wireless orhardwired WAN, LAN, and the like, public communication network, such asthe Internet, and private communication network, such as an intranet.The design architecture of the system enables the system to easilyintegrate with any hardware platform, operating system, and most desktopand enterprise applications. The system is platform, network, andoperating system agnostic.

The system, method, and computer program product of the presentinvention supports a wide range of data and network protocols, includingnative support for IP, XML, IoT, WAP, and other industry standard dataand network protocols. The two application modules of the system,method, and computer program product of the present invention can beimplemented using any operating system including, but not limited toUnix, Linux, VMS, IBM, Microsoft Windows NT, 95, 98, 2000, ME, XP,Vista, 7, 8, and 10, and the like.

Employing neural synchronization processing, the system, method, andcomputer program product of the present invention can transport andprocess any type of data including ASCII Text, EBCIDIC, binary data,such as streaming video, streaming-real-time audio, image data (e.g.,x-ray films), and unicode (i.e., for carrying different dialects oflanguages—e.g., Chinese, Japanese). The system, method, and computerprogram product of the present invention provides access to and deliveryof content and applications to a full range of devices, regardless ofwhether the devices connect over wireline or wireless networks. Itfurther provides the ability to seamlessly service multiple connectionmethods, wired and wireless connectivity service options, and devicetypes (workstations/desktops, handhelds, cell phones, etc.) at the sametime.

The systems, processes, and components set forth in the presentdescription may be implemented using one or more general purposecomputers, microprocessors, or the like programmed according to theteachings of the present specification, as will be appreciated by thoseskilled in the relevant art(s). Appropriate software coding can readilybe prepared by skilled programmers based on the teachings of the presentdisclosure, as will be apparent to those skilled in the relevant art(s).

The foregoing has described the principles, embodiments, and modes ofoperation of the present invention. However, the invention should not beconstrued as being limited to the particular embodiments describedabove, as they should be regarded as being illustrative and not asrestrictive. It should be appreciated that variations may be made inthose embodiments by those skilled in the art without departing from thescope of the present invention.

While a preferred embodiment of the present invention has been describedabove, it should be understood that it has been presented by way ofexample only, and not limitation. Thus, the breadth and scope of thepresent invention should not be limited by the above described exemplaryembodiment.

Obviously, numerous modifications and variations of the presentinvention are possible in light of the above teachings. It is thereforeto be understood that the invention may be practiced otherwise than asspecifically described herein.

What is claimed is:
 1. A system, method, and computer program product for reduction, optimization, security, and acceleration of computer data transmission and processing using neural synchronization architecture operating on one or more computer devices comprising: a motion decimator computer software application operable to: receive a plurality of computer data records/frames in a plurality of structured and unstructured computer data protocols, and perform spatial and temporal decorrelation of the computer data records/frames, and convert decorrelation data to a set of thalamic motion instructions that translates data record/frame components into objects that are classified as being motion or motionless thereby to remove all motionless objects, and apply primitive, multigenerational, and hierarchical prediction to classify objects as being predictable or unpredictable thereby to remove all predictable motion objects, and apply unpredictable thalamic motion instructions to maintain an internal quantum state in memory, and send unpredictable thalamic motion to other software applications on the same or different computer device to synchronize quantum states. a motion reactor computer software application operable to: receive a plurality of thalamic motion instructions from a motion decimator application, and apply thalamic motion instructions to maintain an internal quantum state in memory, and trigger motion replication to regenerate the plurality of computer data records/frames in a plurality of structured and unstructured computer data protocols, and perform spatial and temporal correlation of the computer data records/frames trigger motion aggregation to interface with artificial and human intelligence systems to gather prediction knowledge, and synchronously acknowledge thalamic motion instruction success status to the motion decimator application.
 2. The system, method, and computer program product of claim 1 incorporates a process for multi-record packaging that allows a plurality of data records/frames to be packed into a single network data frame as a dataset.
 3. The system, method, and computer program product of claim 1 incorporates a security system that implements a communication technique that is resistant to decryption and can incorporate advanced security encoding techniques including Ghost Data security and Picture-in-Picture security. 